A MEDICAL DATA SUMMARY INTERFACE SYSTEM

Abstract

Various systems and methods are disclosed relating to the presentation of patient medical data to enable expedited review and / or detailed analysis of patient medical data. A summary screen may be configured to present a summary of the multitude of available data, wherein the summary screen presents data identified to enable rapid diagnosis of a patient's condition, while more detailed information is available for presentation on pop-up screen(s) or navigation to a specific application configured to present information at a greater level of detail / granularity. The summary screen may be configured to present one or more interactive images (e.g., an MRI) in conjunction with text providing a more detailed description of the diagnosis, medical data, and the like. Figure for abstract: Fig 1

Description

Title of the invention: MEDICAL DATA SUMMARY INTERFACE SYSTEM Technical field

[0001] This application relates to systems and techniques facilitating the presentation of medical information regarding a patient. CONTEXT

[0002] The time available to review a medical imaging examination tends to become shorter as the workload and work demands of medical personnel increase (e.g., as the work of radiologists and clinicians intensifies). However, with the continued application of artificial intelligence (AI) and machine learning (ML) systems and technologies, an increasing number of tasks related to medical imaging and the diagnosis of medical conditions are being automated. BRIEF SUMMARY OF THE INVENTION

[0003] Rather than presenting a multitude of information regarding a patient's health condition and, therefore, requiring a medical expert to sift through the wealth of data (e.g., including images), AI / ML technologies may be leveraged to identify / determine information (i.e., findings, potential diagnoses, and the like), summarize the information, and further present the information in the form of one or more summary screens presented on a summary interface. In one embodiment, one or more images may be presented regarding one or more findings related to a patient's condition, wherein the one or more images may be accompanied by a summary of findings.In addition to presenting summary information, the summary screen(s) may be configured to present a higher level of information, for example, as required to enable medical diagnosis of a patient. In addition, the summary screen(s) may include one or more medical images (e.g., an MRI view) along with a text summary of information relevant to the patient's medical condition. The medical image(s) and summary text may include various hyperlinks that, when selected, may cause a pop-up window to be displayed disclosing additional information, such as dedicated views centered on an area of ​​interest (e.g., a targeted finding), wherein the information may be presented in a manner determined to be relevant to further investigation, such as a usable orientation and view type. In addition, interaction with the images . Medical applications allow rotation, zooming, panning, etc. Consequently, medical staff can initially be presented with the summary screen(s), and, in conjunction with the respective selection of text and image manipulation, navigation through the information allows examination at the summary level up to the exploration of detailed information and / or replacement of the summarized information with a traditional screen presenting the multitude of data as well as access / presentation of a dedicated application with which the doctor can interact more. Consequently, even if there may be a multitude of patient condition data available to be presented to a medical expert, it is possible to take advantage of the application of AI / ML technologies to enable a specialist doctor to quickly establish a diagnosis and, furthermore, reduce the degree of unnecessary interaction of the medical expert with patient data.

[0004] The background described above is merely intended to provide a contextual overview of certain current issues and is not intended to be exhaustive. Other background information may become more apparent after reviewing the following detailed description. DESCRIPTION OF FIGURES

[0005] One or more embodiments are described below in the detailed description section with reference to the following drawings.

[0006] [Fig. 1] illustrates a system that may be used to control the presentation of information and images, according to at least one embodiment.

[0007] [Fig.2] illustrates a patient selection screen, according to one embodiment.

[0008] [Fig.3] illustrates a summary screen, according to one embodiment.

[0009] [Fig.4] shows a summary screen, according to one embodiment.

[0010] [Fig.5] shows a summary screen, according to one embodiment.

[0011] [Fig.6] shows a summary screen, according to one embodiment.

[0012] [Fig.7] illustrates subcomponents included in a state component, in accordance with one or more embodiments.

[0013] [Fig.8] presents a diagram illustrating the operational flow for examining the medical condition of a patient, in accordance with one or more embodiments.

[0014] [Fig.9] presents a computer-implemented methodology for generating / present a summary of medical information for review, in accordance with one embodiment.

[0015] [Fig. 10] is a block diagram illustrating an exemplary computing environment in which the various embodiments described herein may be implemented.

[0016] [Fig. 11] is a block diagram illustrating an example of an in-environment computer with which the disclosed object can interact, according to one embodiment. DETAILED DESCRIPTION

[0017] The following detailed description is merely illustrative and is not intended to limit the embodiments and / or the application or uses of the embodiments. Furthermore, there is no intention to be bound by the information expressed and / or implied in the preceding background section and / or detailed description section.

[0018] One or more embodiments are now described with reference to the drawings, in which like reference numerals are used to designate like elements throughout. In the following description, for the purpose of explanation, numerous specific details are presented in order to provide a more thorough understanding of the one or more embodiments. It is obvious, however, in various instances that the one or more embodiments may be practiced without these specific details.

[0019] It is to be understood that when an element is referred to as "coupled" to another element, it may describe one or more different types of coupling, including, but not limited to, chemical coupling, communicative coupling, electrical coupling, electromagnetic coupling, operational coupling, optical coupling, physical coupling, thermal coupling, and / or another type of coupling. Similarly, it is to be understood that when an element is referred to as "connected" to another element, it may describe one or more different types of connection, including, but not limited to, electrical connection, electromagnetic connection, operational connection, optical connection, physical connection, thermal connection, and / or another type of connection.

[0020] As used herein, the term "data" may include metadata. Further, the ranges A to n are used herein to indicate a respective plurality of devices, components, signals, etc., where n is any positive integer.

[0021] An advantage of the one or more systems, computer-implemented methods, and / or computer program products disclosed herein may be to enable the presentation of a summary of a patient's data allowing a healthcare professional to quickly access information to enable an efficient and timely diagnosis of the patient's condition, while minimizing the time the healthcare professional has to interact with the data to make the diagnosis. Accordingly, it is possible to leverage AI / ML technology to analyze / summarize the patient's data and limit an initial presentation of the patient's data to information relevant to the diagnosis, wherein the initial presentation of the patient's data may include one or more findings, which, upon initial or subsequent examination, may be used to assist in the determination of one or more diagnoses.

[0022] It should be noted that while the various embodiments presented herein are directed to the application of AI / ML to summarize and present medical data, the embodiments are also applicable to any comparable use, for example, presentation of veterinary information, presentation of financial data, troubleshooting, and the like.

[0023] Referring now to the drawings, [Fig.l] illustrates a system 100 that may be used to control the presentation of information and images, according to at least one embodiment.

[0024] The system 100 includes a medical data presentation system (MDPS) 110. As illustrated, the MDPS 110 can be configured to receive information and data regarding a patient's 102 condition, where the information can include patient data 105A-n, medical data 106A-n, and / or images 107A-n. For the sake of readability, the patient data 105A-n, the medical data 106A-n, and / or the images 107A-n are combined on the [Fig.l] as input data 108A-n. It should be noted that while the term input data 108A-n is used, any of the patient data 105A-n, the medical data 106A-n, and / or the images 107A-n can be discussed. Additionally, the terms patient data 105A-n, medical data 106A-n, images 107A-n, and / or input data 108A-n can be used interchangeably.Therefore, although a specific embodiment mentions images 107A-n, the embodiment may also relate to patient data 105A-n, medical data 106A-n and / or input data 108A-n.

[0025] The patient data 105A-n may include the name, address, contact information, date of birth, gender, identifier (e.g., social security number, national health service identification, etc.), and the like, regarding the patient 102. The medical data 106A-n may include medical condition data regarding the patient 102, medical history, medication history, heart rate, blood levels, and the like. The images 107A-n may include any images associated with the treatment of the patient 102, e.g., magnetic resonance imaging (MRI), an x-ray image, a mammogram image, scans, and the like.

[0026] The MDPS 110 may further include a presentation component 115 configured to control the presentation of any data 108A-n on a display (e.g., display 187). In one embodiment, the presentation component 115 may be configured to control whether the data 108A-n is presented in a largely comprehensive manner, for example, the patient data 105A-n, medical data 106A-n and / or images 107A-n are presented in their entirety on one or more all-data screens 160A-n or in summary form, for example on one or more summary screens 150A-n. The presentation of an entirety of input data 108A-n on the all-data screens 160A-n is typical of a conventional system / approach, however, the volume of data can make the task of diagnosing a medical condition by a physician 103 time-consuming and also potentially fraught with diagnostic errors given the wealth of data to be reviewed. However, using the one or more embodiments presented herein relating to the identification and presentation of summary data allows the physician 103 to quickly, accurately and confidently determine whether a medical problem exists or not.The term physician 103 is used herein to represent any person involved in reviewing medical data, one or more results, and making a diagnosis based thereon, such as a physician, nurse, medical specialist, health care professional, etc.

[0027] In another embodiment, the presentation component 115 may be configured to use the summary screens 150A-n to present one or more portions of the total information available to the patient 102.

[0028] The MDPS 110 may further include a summary component 120, wherein, in one embodiment, the summary component 120 may be configured to control the presentation / description of the patient data 105A-n, medical data 106A-n, images 107A-n, and the like, for example, on the summary screens 150A-n. The summary screens 150A-n may respectively include one or more image areas 170A-n configured to include one or more images 172A-n. The images 172A-n may include images 107A-n relating to the patient 102 (for example, as part of the input data 108A-n), as well as images identified and extracted from the historical data 194A-n / medical data 196A-n. Further, the summary screens 150A-n may respectively include one or more text boxes 174A-n configured to include one or more texts 176A-n.In one embodiment, the text 176A-n may include one or more results 179A-n, wherein the results 179A-n may be automatically generated by the status component 130. The text 176A-n may include text, alphanumeric characters, values, symbols, etc., in the input data 108A-n relating to the patient 102, as well as the text / information identified and extracted from the historical data 194A-n / medical data 196A-n. The summary screens 150A-n may further include one or more potential diagnoses 178A-n automatically generated by the status component 130, wherein the diagnoses po . potentials 178A-n may then be accepted / rejected by the physician 103. It should be noted that the terms diagnosis / diagnoses 178A-n and results 179A-n are used interchangeably herein, indicating that a result 179A-n may become a diagnosis 178A-n, and further, a diagnosis 178A-n may be derived from a result 179A-n. In addition, as shown in [Fig.l], additional information 155A-n may be present, whereby the additional information 155A-n may include available text, information, and images that, while not currently presented on a respective summary screen 150A-n, are available for presentation.For example, while the first text 176A is presented on the summary screen 150A, the first text 176A being a summary, the first text 176A may include one or more links (e.g., hyperlinks) that, when selected, may cause one or more portions (e.g., images, text) of the additional information 155A-n to be presented on the summary screen 150A-n (e.g., as a pop-up window). In one embodiment, the presentation component 115 may be configured to determine whether the data 108A-n is to be presented on an all-data screen 160A-n or on a summary screen 150A-n, and the summary component 120 may be configured to control how a summary of data 108A-n is presented on a summary screen 150A-n.In one embodiment, the summary component 120 may further be configured to identify respective images 172A-n to be presented on the summary screens 150A-n. For example, while multiple images 172A-n may be available for presentation on a summary screen 150A-n, the summary component 120 may be configured to identify an image 172A-n that allows the physician 103 to make an accurate / quick decision (e.g., a diagnosis 178A-n) regarding a medical condition of the patient 102. For example, as described in more detail, a status component 130 may determine a result 179A-n and / or a proposed diagnosis 178A-n.The summary component 120 may receive the result 179A-n, for example, and based on the result 179A-n, in conjunction with one or more processes 125A-n, the summary component 120 may identify an image 172A-n applicable to the result 179A-n (e.g., allows the physician 103 to easily discern the result 179A-n), and present the identified image 172A-n on a summary screen 150A-n.

[0029] As further illustrated, the MDPS 110 may further include a status component 130 configured to automatically examine some or all of the input data 108A-n to determine / provide an indication, e.g., a result 179A-n, of whether or not the patient 102 has a health condition of concern. As described in more detail, during the generation of results 179A-n and / or diagnosis 178A-n, the data 108A-n may be supplemented with medical data 196A-n (e.g., in memory 184), wherein the medical data 196A-n may comprise the body of available medical knowledge. Accordingly, as part of a diagnostic process, the status component 130 may be configured to compare the characteristics / criteria / variables / parameters present in the data 108A-n to the medical data 196A-n regarding the potential medical condition of the patient 102.

[0030] As described in more detail, the MDPS 110 may include a set of processes 125A-n, wherein the processes 125A-n may include respective AI and ML technologies and techniques. The processes 125A-n (i.e., an application) may be directed toward the multitude of medical conditions that humans have recognized (e.g., according to the medical data 196A-n), and therefore, the medical conditions that the patient 102 may encounter during their lifetime.

[0031] The MDPS 110 may further include a report component 135 configured to generate a report 165A-n. In one embodiment, as described in more detail, the report 165A-n may be a structured document generated based on the information presented on the summary screen 150A-n, wherein the summary screen 150A-n may function as a display window. The report 165A-n may be of any suitable file format / type, such as a PDF, a DICOM PDF, a word processing document, a spreadsheet, and the like. As described in more detail, the report 165A-n may be configured to be presented on the summary screen 150A-n, printed by a printer (not shown) associated with the MDPS 110, exported to a remote system (e.g., via the I / O 188), and the like.In one embodiment, the report 165A-n may function as a medical record regarding the medical condition of the patient 102 and other respective procedures and as an examination used by the physician 103 to diagnose the medical condition of the patient 102. Accordingly, the report 165A-n may function as a digital file regarding the patient 102. In one embodiment, the respective results 179A-n generated by the condition component 130 may be presented in the report 165A-n together with the diagnosis 178A-n confirmed by the physician 103, such a report format may be provided to a patient or other entity concerned with the diagnosis 178A-n of the patient 102 and the data (e.g., the results 179A-n) leading to the diagnosis.In another embodiment, the report 165A-n may present the diagnosis 178A-n developed by the physician 103 together with one or more respective results 179A-n and any interactions / modifications / annotations made by the physician 103 in developing the diagnosis 178A-n.

[0032] In one embodiment, the MDPS 110 may include a confirmation component 140. In order to ensure compliance with respective healthcare standards, and legal standards, etc., that govern the healthcare entities res prospects in the world, the confirmation component 140 may be used to ensure that the physician 103 has reviewed the respective information, results 179A-n, and any potential diagnosis 178A-n, etc., generated and presented by the MDPS 110 (e.g., via the summary screen 150A-n or the all data screen 160A-n) and has signed off (e.g., confirmation 142A-n) on the diagnosis 178A-n / condition / result 179A-n of the patient 102, as represented by the information, diagnosis 178A-n, result 179A-n, etc., as generated and presented by the MDPS 110. In an exemplary embodiment, the physician 103 may log in to the MDPS 110 (e.g., with their respective credentials, password, etc.), review / interact with the information presented on the summary screen 150A-n, and when the physician 103 is satisfied / agreed that the MDPS 110 (or the physician 103) has accurately identified and / or diagnosed the one or more medical conditions of the patient 102, the physician 103 may sign the information with the confirmation 142A-n. In the event that the physician 103 signs the information, etc., the report component 135 may be configured to generate a report 165A-n.

[0033] In an exemplary implementation, the condition component 130 may be configured to analyze the data 108A-n, and although the initial review of the data 108A-n may be focused on a first medical condition, the review may also be configured to analyze other conditions that may not initially be of concern to the physician 103. For example, the patient 102 has been involved in a car accident and respective medical procedures (e.g., imaging) are being performed to determine whether the patient 102 is suffering from internal bleeding, whether their organs (e.g., liver, kidneys, etc.) have been damaged as a result of the accident, etc. Accordingly, a first series of processes 125A-n is used by the condition component 130 to identify and assess the degree of bodily injury resulting from the car accident, e.g., by generating one or more results 179A-n regarding a first patient condition.During the evaluation of the organs, an unexpected mass may be detected in the one or more images 107A-n. This may automatically trigger, at the status component 130, the implementation of a second set of processes 125A-n, wherein the second set of processes 125A-n is configured to determine the presence of a cancerous growth / tumor, as presented in one or more results 179A-n regarding the second condition of the patient. Therefore, while the patient 102 is initially evaluated for a first condition, the application of the set of various processes 125A-n allows for rapid detection / determination of a second condition, the presence of which was not initially anticipated by the physician 103.

[0034] In general, about 50-90% of AI / ML examinations of performed medical procedures indicate that the patient 102 does not have a pre-existing medical condition. occupying, for example, a mammography procedure indicates that the patient 102 does not have cancer. In a conventional approach, the physician 103 may have to review a multitude of medical data of the patient 102 (e.g., presented on an all-data screen 160A-n), which may be time-consuming. By using one or more processes 125A-n that are directed to the medical condition of interest (e.g., cancer), information including medical data 106A-n and / or images 107A-n may be specifically targeted by the processes 125A-n. Accordingly, the relevant information, e.g., images 172A-n and text 176A-n including the results 179A-n identified by the processes 125A-n, may be presented on the summary display 150A-n by the summary component 120.Given the nature of the targeted images 172A-n and text 176A-n, the physician 103 can examine the data presented on the summary display 150A, in the form of a diagnosis 178A-n, and close by using the confirmation component 140, and the report 165A-n can be generated. According to the embodiments presented herein, by applying the summary component 120, processes 125A-n, results 179A-n and using the summary display 150A-n, the physician 103 can quickly determine that a medical condition of interest does not exist. Therefore, since 50-90% of medical procedures generate negative results, the physician 103 is able to spend minimal time examining the health status of the patient 102, thereby freeing up the time of the physician 103 to attend to other issues, e.g., patient care issues, in the hospital, in an ambulance, etc.

[0035] As illustrated, the system 100 may further include historical data 194A-n, wherein the historical data 194A-n may include previously used summary screens 150A-n, previously presented images 172A-n, previously presented text 176A-n, prior results 179A-n, prior interaction by one or more physicians 103A-n with medical data 196A-n, additional information 155A-n, information presented on the respective summary screens 150A-n, image areas 170A-n, images 172A-n, text areas 174A-n, text 176A-n, during report generation 165A-n, and the like. Accordingly, 125A-n processes may be trained based on prior interactions with particular information by a 103A-n physician.Therefore, when the first text 176F has been previously presented on the summary screen 150A-n, if additional information has been sought by the physician 103A-n, the importance of the additional information (e.g., the medical data 106F) can be established so that after some repeated / separate interaction instances, a process 125F associated with the first text 176F can be trained to present the medical data 106F in the summary area. textual 174A-n. Further, by frequently training the respective processes 125A-n, as new research is obtained regarding a parameter / measurement / criterion that is determined by the medical community to play a more important / less important role in diagnosing a condition, the respective processes 125A-n can be trained to reflect the greater / lesser importance of a particular criterion, outcome 179A-n, etc., in diagnosing a condition, and whether information relating to the particular criteria should be included / removed when creating the summary screen 150A-n by the summary component 120.

[0036] It should be noted that the various processes 125A-n and operations presented herein are merely examples of respective AI and ML operations and techniques, and any suitable AI / ML model / technology / technique / architecture may be used in accordance with the various embodiments presented herein. In one aspect, the processes 125A-n may operate alone or in combination to create one or more applications configured to be implemented regarding the identification of a particular medical condition, e.g., a set of processes 125A-n forming an application for identifying issues related to the cardiovascular condition of patient 102. The processes 125A-n may be based on the application of terms, expressions, criteria, parameters, variables, and the like, in the input data 108A-n, historical data 194A-n, medical data 196A-n, etc.The summarization component 120 may be used to implement the processes 125-n in conjunction with the MDPS 110 and any components included in the MDPS 110. An example of the process 125A-n may include a vectorization technique such as bag-of-words (BOW) text vectors, and further, any suitable vectorization technology may be used, e.g., Euclidean distance, cosine similarity, etc. Other suitable AI / ML technologies / processes 125A-n that may be applied include, but are not limited to, any vector representation via term frequency-inverse document frequency (tf-idf) capturing the frequency of terms / tokens in the input data 108A-n relative to terms / tokens present in the medical data 196A-n, historical data 194A-n, etc.Other applicable AI / ML technologies include, but are not limited to, neural network embedding technologies, layered vector representation of terms / categories (e.g., common terms having different times), bidirectional autoregressive transformer (BART) model architecture, bidirectional encoder representation from transformers (BERT) model, diffusion model, variational autoencoder (VAE), generative adversarial network (GAN), language-based generative model such as large language model (LLM), trans . pre-trained generative trainer (GPT), long short-term memory (LSTM) network / operation, sentence-state LSTM (S-LSTM), deep learning algorithm, sequential neural network, sequential neural network that allows persistent information, recurrent neural network (RNN), convolutional neural network (CNN), neural network, capsule network, machine learning algorithm, natural language processing (NLP) technique, sentiment analysis, bidirectional LSTM (BiLSTM), stacked BiLSTM, and other analogs. Accordingly, in one embodiment, the implementation of the summary component 120, the presentation component 115, the status component 130 and the like, allows for plain / natural language programming / annotation / correlation of the input data 108A-n with medical data 196A-n, and / or historical data 194A-n, etc., to generate the summary images 172A-n and the text 176A-n presented on summary screens 150A-n.

[0037] Language models, LSTM, B ART, etc., can be formed with a very complex neural network, for example, comprising billions of weighted parameters. Training of the language models, etc., can be performed, for example, by the presentation component 115, the summary component 120, the state component 130, etc., with data sets, whereby the data sets can be trained using any suitable technology, such as data in historical data 194A-n, medical data 196A-n, input data 108A-n and the like.Further, as mentioned previously, the historical data 194, medical data 196, input data 108A-n, patient data 105A-n, medical data 106A-n, images 107A-n, text 176A-n, images 172A-n, additional information 155A-n, and the like, may include text, alphanumeric characters, numbers, single words, sentences, short instructions, long instructions, expressions, syntax, source code instructions, machine code, etc. Fine-tuning a process 125A-n may include applying historical data 194, medical data 196, input data 108A-n, images 172A-n, text 176A-n, results 179A-n, additional information 155A-n, and the like, to the process 125A-n, the process 125A-n is adjusted accordingly by applying the historical data 194, etc., so that, for example, the weights in the respective process 125A-n are adjusted by applying the historical data 194, and the like. As new information (e.g., the input data 108A-n is processed), the historical data 194 can be updated accordingly and, furthermore, the processes 125A-n can be refined.

[0038] In accordance with one or more embodiments, and without limitation, the physician 103 may perform any of the following interactions with the summary screens 150A-n:

[0039] a) The physician 103 may select an examination presented in a work list (e.g., one or more tasks to be performed regarding the examination of the condition of the patient 102), in which the physician 103 may select the all data screen(s) 160A-n, and / or the summary screen(s) 150A-n, as part of the examination process.

[0040] b) The summary screens 150A-n may, at a minimum, be configured to present a single large display window that provides one or more previews of the respective graphical objects / images 172A-n / results 179A-n that have been automatically identified by any of the presentation component 115, the summary component 120, and / or the status component 130, in conjunction with the processes 125A-n.

[0041] c) The physician 103 can perform various manipulations of the images 172A-n. For example, image paging, image zooming, image moving, image rotating, changing the viewing type / rendering mode, etc.

[0042] d) As mentioned previously, the summary screens 150A-n may include a text area 174A-n (e.g., a text panel display portion) which may include text 176A-n, etc., providing a summary of the results 179A-n. Respective portions of the text 176A-n may be highlighted / linked to provide links to additional information 155A-n. According to [Fig.l], while the text area 174A-n is shown as being located on the right side of the summary screen 150A, the text area 174A-n may be present on any area of ​​the summary screen 150A. As mentioned previously, the text presented in the text area 174A-n may include text / information identified by the summary component 120.The physician 103 may select the respective links in the text 176A-n, after which upon selection of a respective link, a pop-up window (partial summary screen 150B) may be presented on the summary screen 150A, wherein the pop-up window may be configured to be positioned on the summary screen 150A.

[0043] e) The physician 103 may "hover" the mouse cursor, or the like, over the text 176A-n, whereby respective thumbnail images 172A-n / 107A-n relating to the text 176A-n may be presented.

[0044] f) The physician 103 may select a link to a process 125C dedicated to a specific aspect of the condition of the patient 102. For example, when the physician 103 examines a summary of the data, in the results 179A-n, concerning a patient 102 suffering from a cardiac condition, the summary screen 150C may present respective aspects of cardiac condition, e.g., image 172C, results 179A-n and relevant text 176C. However, a series of related processes 125A-n may be available and selected by clicking on the respective links.For example, process 125D may include one or more algorithms / applications configured to analyze one or more issues regarding a calcium score for patient 102, process 125E may include one or more algorithms / applications configured to analyze one or more issues regarding coronary analysis of the coronary system of patient 102, process 125F may include one or more algorithms / applications configured to analyze one or more issues regarding stenosis of the coronary artery system of patient 102, process 125G may include one or more algorithms / applications configured to analyze one or more issues regarding a coronary flow reserve (FFR) measurement of the coronary region of patient 102, and the like.When each process 125A-n is selected, the information / results 179A-n provided by the process / application may be presented on the summary screen 150A-n. Therefore, while the text 176A-n on the summary screen 150A may summarize the respective analyses / results 179A-n of the processes 125A and 125B, a series of associated processes 125C-n are available for use by the physician 103 to further examine the medical condition of the patient 102.

[0045] g) The text 176A-n presented on the screen 150A-n / pop-up window may be configured to be editable so that the physician 103 may modify / annotate the text as needed. The physician 103 may interact with the summary screen 150A-n by any suitable means, each via a keyboard / interface (not shown) included in the HMI 186, and the like, a microphone (not shown) incorporated in the HMI 186, and the like, so that the physician 103 may dictate text and / or instructions. The information presented on the summary screen 150A-n (e.g., text 176A-n, images 172A-n, results 179A-n) may be copied / pasted into a report portion 165A-n of the summary display 150A-n (as shown in [Fig. 6]).

[0046] h) The physician 103 can interact with the images 172A-n, so that by hovering / placing a cursor over the respective results on the image area 170A-n, the corresponding text 176A-n in the text area 174A-n can also be highlighted.

[0047] i) The physician 103 may further delete a result 179A-n from the text area 174A-n, for example by right-clicking the mouse / cursor on the unwanted text 176A-n. By deleting the result 179A-n from the text area 174A-n, the result 179A-n may also be removed from inclusion in the report 165A-n, for example by preventing the result 179A-n / text 176A-n from being included in the exported report 165 An.

[0048] j) The summary screen 150A-n may also include an image library tab (for example, the image library tab 410, according to [Fig.4]), which, when selected, allows the doctor 103 to retrieve all the images / respective images 172A-n / images 107A-n / images available in the historical data 194A-n and / or in the medical data 196A-n to generate automatically for presentation on the summary screen 150A-n. The respective images 172A-n, etc., may include all the images that the doctor 103 has added to the image set 172A-n, for example by using a copy tool in the clipboard (screen capture). In addition, the doctor 103 can interact with the images 172A-n, etc., to facilitate the addition of images and / or the deletion of images from the summary screen 150A-n, which may in turn lead to the addition / deletion of the respective image 172A-n, etc., to / from the report 165A-n.Further, the physician 103 may select an image 172A-n, etc., to copy / paste the image into the report 165A-n.

[0049] k) The summary screen 150A-n may further include a tab allowing a preview of the report 165A-n, e.g., before the report 165A-n is selected, as well as an ability to view any document (e.g., a medical report / paper) that may relate to particular information, e.g., a result 179A-n, presented on the summary screen 150A-n.

[0050] As further illustrated, the MDPS 110 may be communicatively coupled to a computer system 180. The computer system 180 may include a memory 184 that stores the respective computer-executable components (e.g., the presentation component 115, the summary component 120, the processes 125A-n, the status component 130, the report component 135, the confirmation component 140, the vector component 710, the similarity component 720, and the like) and further, a processor 182 configured to execute the computer-executable components stored in the memory 184.The memory 184 may further be configured to store patient data 105A-n, medical data 106A-n, images 107A-n, input data 108A-n, historical data 194, medical data 196, images 172A-n, text 176A-n, results 179A-n, diagnoses 178A-n, reports 165A-n, additional information 155A-n, similarity indices Sl-n, vectors Vn (as described in more detail herein), and the like. The computer system 180 may further include a human-machine interface (HMI) 186 (e.g., a display, a graphical user interface (GUI)) that may be configured to present various information, including summary screens 150A-n, all data screens 160A-n, receive text / dictation instructions, mouse / cursor inputs, keyboard inputs, and the like.The 186 HMI may include a 187A-n interactive display / screen to present the various summary screens. 150A-n, the all data screens 160A-n, the reports 165A-n, the additional information 155A-n and the like. The computer system 180 may further include an I / O component 188 for receiving and / or transmitting historical data 194, medical data 196, input data 108A-n, results 179A-n, diagnostics 178A-n, reports 165A-n and the like.

[0051] To better understand the various embodiments presented herein, in Figures 2-6, images 200-600 show screenshots of a series of example screens, in accordance with one or more embodiments.

[0052] In [Fig. 2], image 200 shows a patient selection screen. In one embodiment, the patient selection screen 200 (also referred to herein as a worklist, according to [Fig. 8]) shows a list of respective patients, as well as a link to present information regarding the patient 102 in the form of a summary review (e.g., by summary screens 150A-n) rather than as an all data screen 160A-n. During selection of a patient record 102, an image 172A relating to the patient 102 may be presented on the selection screen 200. Further, more than one medical condition may relate to the patient 102, such that the patient selection screen 200 may include a series of medical conditions / diagnostic links which, when selected, may initiate implementation of respective processes 125A-n relating to the particular medical condition(s) of the patient 102.

[0053] In [Fig. 3], image 300 shows a summary screen 150A, wherein the summary screen 150A may be an initial summary screen generated by the summary component 120 in conjunction with processes 125A-n. The summary screen 150A may be presented in response to selecting the patient's medical record 102 on the selection screen 200. As illustrated, the summary screen 150A may include the patient's data 105A, and further include a first screen area, image area 170A-n, wherein the image area 170A-n may include various images 172A-n relating to the medical condition of the patient 102. For example, image area 170A includes image 172A, image area 170B includes image 172B. The summary screen 150A may further include a second screen area, the text area 174A-n, wherein the text area 174A includes the text 176A. The results 179A-n may be presented on the summary screen 150A-n.

[0054] In [Fig.4], image 400 shows a summary screen 150B, wherein the summary screen 150B is an updated version of the initial summary screen 150A, for example updated based on a hyperlink selected in the text area 174A-n. New images 172C-E are presented in pop-up window image areas 170C-E overlapping with the initially displayed image areas presented 170A-B and originally presented images 172A-B. As shown in [Fig.4], a selection of thumbnails is presented in the updated text box 174B which, when selected, can cause the presentation of images 172C-E.

[0055] In [Fig. 5], image 500 shows a summary screen 150C, wherein summary screen 150C represents image area 170A / image 172A, while image area 170F shows images 172G and 172H. Images 172G and 172H may be presented in response to selection of a link in an image (e.g., in image 172A), a link in summary text 176C, and the like.

[0056] In [Fig. 6], image 600 shows a summary screen 150D, wherein the summary screen 150D includes a rendering of the report 165A and, further, a text box 174A. As mentioned previously, the report 165A may be generated based on the selection of a review completion tab, for example as detected by the confirmation component 140. The respective report pages 165A may be selected for review.

[0057] Returning to [Fig.l], the state component 130 (e.g., in conjunction with the processes 125A-n) may further be configured to automatically identify one or more features in the input data 108A-n, the historical data 194, and / or the medical data 196, wherein the term "one or more features" refers to any of a term, a value, a phrase, a variable, a parameter, a criterion, an image representation, a medical condition, a medical reading, an annotation (e.g., by the physician 103 interacting with the images 172A-n, the results 179A-n, and / or the text 176A-n), an image selection / deletion (e.g., by the physician 103 interacting with the images 172A-n), and other analogs, concerning the patient 102 which may be present in any input data 108A-n, historical data 194, medical data 196.

[0058] Any suitable technology, methodology, and the like may be used to identify one or more characteristics relating to a medical condition of the patient 102. In one aspect, at the time the input data 108A-n is received at the MDPS 110, knowledge of the respective one or more characteristics relating to the patient 102 represented in the input data 108A-n may be limited / unknown, for example, by one or more physicians 103A-n.

[0059] In an exemplary embodiment, the state component 130 may be configured to compare a degree of similarity S between a feature in the input data 108A-n with the set of features in the historical data 194 and / or the medical data 196 stored in the memory 184. In an exemplary embodiment, the similarity S may range from a low degree of similarity (e.g., close to 0 in a similarity system) to a 1 indicating no similarity (e.g., close to 0 in a similarity system) of 0 to 1 indicating no similarity. match) up to a high degree of similarity (e.g., close to 1.0 in a 0-to-1 similarity system indicating a match), and any intermediate degree of similarity between them.

[0060] In [Fig. 7], the system 700 further illustrates the subcomponents included in a state component, in accordance with one or more embodiments. As shown in [Fig. 7], the state component 130 may include a vector component 710 configured to process / vectorize the respective features in the input data 108A-n, the historical data 194, the medical data 196, etc. In processing the respective features of the input data 108A-n, the historical data 194, etc., each respective feature in each of the input data 108A-n, the historical data 194, etc., may be defined / re-presented by the vector component 710 as a vector Vn in which the vector scheme used may be any one of a two-dimensional vector and a multi-dimensional vector (e.g., a multi-dimensional vector).The greater the similarity between a first vector representation VI and a second vector representation V2, the more it can be inferred that the element represented by the first vector representation VI relates to the element represented by the second vector V2. The respective vectors Vn may be generated using any suitable approach, for example, the respective features may be expressed numerically, for example, any of the state component 130, the summary component 120, the presentation component 115, and the like, for example, together with one or more processes 125A-n may be configured to identify a feature, and the vector component 710 converts one or more alphanumeric / text / numeric / symbols / content / annotations portions of the respective features into the input data 108A-n, the historical data 194, etc., into vectorized content.

[0061] The state component 130 may further include a similarity component 720 configured to determine a degree of similarity S (e.g., a similarity index Sl-n) between the vector representation VI of a feature in the input data 108A-n and a vector representation V2 of a feature in the historical data 194 and / or medical data 196 that have been previously identified / vectorized. For example, the state component 130 identifies the respective terms calcium score, stenosis data, coronary data, FFR and the like in the input data 108A-n as well as the identical or potentially similar terms in the historical data 194 and the medical data 196. The respective terms may be vectorized by the vector component 710, such that in one example input data 108A has a parameter "calcium score" while the historical data 194 has a parameter "calcium measurement", the calcium score may be represented as a vector VI, while the calcium measurement associated with a medical condition of interest in the historical data 194 and / or the medical data 196 may be represented as a vector V2. In the event that S indicates a high degree of confidence in the similarity that the "calcium score" relates to the "calcium measurement", an associated result 179A-n may be presented, and if necessary, further review / analysis by the condition component 130 may be performed regarding the medical condition of the patient 102 (e.g., is the calcium score of the patient 102 of concern?). For relevant comparable parameters, the condition component 130 may be further configured to compare respective values / measurements for the particular parameter to determine whether the patient 102 is suffering from a medical condition.In the event that S indicates a low degree of confidence in the similarity that the "calcium score" relates to the "calcium measurement," a closer examination of the input data 108A-n, historical data 194, and medical data 196 may be performed to identify values, for example, in a result 179A-n, that relate to the medical parameter / condition of concern. In one embodiment, the degree of confidence in S may be presented on the summary screen 150A-n in conjunction with a diagnosis 178A-n to which the degree of confidence relates. Accordingly, the physician 103 may readily determine and consider the degree of confidence with which the status component 130 identified results 179A-n and / or determined the diagnosis 178A-n approved by the physician 103.

[0062] According to [Fig.7], the state component 130 may further be configured to generate a notification 750A-n indicating a diagnostic status of one or more medical conditions of the patient 102. The notification 750A-n may be applied to the summary screen 150A-n, for example embedded in text 176A-n, informing the physician 103 whether the results 179A-n indicate that the patient 102 is suffering from a particular condition. The physician 103 may further examine the information presented, or available to be presented on the summary screen 150A-n to confirm / investigate the results 179A-n of the one or more components included in the MDPS 110. As mentioned previously, the physician 103 may generate a confirmation of the diagnosis 178A-n via the confirmation component 140.

[0063] In [Fig.8], diagram 800 illustrates the operational flow for diagnosing a patient's medical condition, in accordance with one or more embodiments. The following includes various operations 1 to 4 and various activities that may be respectively performed. It should be noted that the numbering of operations 1 to 4 is arbitrary and any sequence of operations / activities may be performed when a physician 103 uses the data / information presented on the one or more summary screen(s) 150A-n and on the full data screen(s) 160A-n.

[0064] In step 800-1, an initial status of the patient diagnosis 102 may be established. As mentioned previously, input data 108A-n may be available for the patient 102 and received at the MDPS 110. According to [Fig. 2], the input data 108A-n for the patient 102 may be presented on the patient selection screen 200, together with medical data for other patients. Upon selection of the patient 102, the respective processes 125A-n may be initiated (e.g., by the presentation component 115, the status component 130, the summary component 120, etc.) to initiate the review of the input data 108A-n of the patient 102 regarding one or more potential medical conditions. The processes 125A-n may be used to examine the input data 108A-n to identify various anatomical markers in the input data 108A-n, thereby enabling the implementation of the respective processes 125A-n.

[0065] At step 800-2, in an exemplary operating scenario, the physician 103 may choose to review the input data 108A-n and the respective results 179A-n, the proposed diagnosis 178A-n, etc., via a full data screen 160A-n. As mentioned previously, the full data screen 160A-n embodies a conventional approach to reviewing and diagnosing medical information (e.g., input data 108A-n) for the patient 102. The medical information is available for the physician 103 to review in what is effectively a step-by-step manner as the physician 103 reviews the images, results, and medical / medical information, while attempting to diagnose the condition of the patient 102.

[0066] At step 800-3, according to one or more embodiments presented herein, rather than presenting the entire input data 108A-n and all diagnoses, associated data, etc., one or more components included in the MDPS 110 may be configured to present medical data regarding the patient 102 in a summary form via the summary screen 150A-n. As previously described, a summary component 120 in conjunction with the processes 125A-n may be used to summarize the wealth of information available (e.g., in the input data 108A-n, historical data 194, medical data 196, results 179A-n, etc.). As shown in [Fig.3], the summary screens 150A-n and the full data screens 160A-n may interface so that, even though the information is presented in a full rendering of the full data screens 160A-n, the same information may also be summarized on the summary screens 150A-n. as being presented in its entirety via a pop-up window presented on the summary screen 150A-n, and also a temporary presentation of the full data screen 160A-n for y . edit the information, before returning to the summary view. In the event that the physician 103 wishes to make a change to the information, the change (e.g., text, voice command, etc.) may be applied to the summarized information on the summary screen 150A, with the change being carried over to the full information available for presentation on the full data screen 160A-n. Therefore, while the physician 103 may interact with summarized information (e.g., results 179A-n), the MDPS 110 is configured to ensure that the changes are captured. In another embodiment, while the physician 103 may interact with the summarized information on the summary screen 150A, the full information available regarding the particular medical condition may be provided on the summary screen 150A in a manner comparable to the information presented on a full data screen 160A.Once the physician 103 has made the required changes, the summary screen 150A may return to presenting a summary of the available information.

[0067] In another embodiment, while on the summary screen 150A-n, the physician 103 may request that a new analysis be performed, for example, which may not have been performed at present. The status component 130 may implement respective processes 125A-n to enable the analysis to be performed, the results (a) being summarized on the summary screen 150A-n while a complete set of information generated from the new analysis is available via the complete data screen 160A-n.

[0068] At step 800-4, as previously described, the information (e.g., in the input data 108A-n, historical data 194, medical data 196, results 179A-n, proposed diagnosis 178A-n, etc.) may be reviewed by the physician 103, and upon completion of the review, a report 165A-n may be generated. The report 165A-n may be generated for integration / use by any suitable technology / application, e.g., a rural integrated services system (RISS), a picture archiving and communication system (PACS), and the like. The report 165A-n may be printed and / or exported to an external system.

[0069] [Fig.9] shows a computer-implemented methodology 900 for generating / presenting a summary of medical information for review, in accordance with one embodiment.

[0070] In step 910, medical data (e.g., input data 108A-n) for a patient (e.g., patient 102) may be received at a medical data presentation system (MDPS) (e.g., MDPS 110).

[0071] At step 920, various AI / ML techniques and technologies (e.g., processes 125A-n) may be applied (e.g., by the pre-component summary component 115, summary component 120, condition component 130) to the medical data to (a) identify one or more medical conditions from which the patient may be suffering, and (b) to generate summary data (i.e., first medical information, e.g., images 172A-n, text 176A-n, results 179A-n). As mentioned previously, a multitude of medical information (i.e., second medical information) is available (e.g., input data 108A-n, historical data 194A-n, medical data 196A-n) from which the one or more medical conditions of the patient are available can be determined and a diagnosis (e.g., diagnosis 178A-n) is derived.The various AI / ML techniques may be used to facilitate pre-processing of patient data (input data 108A-n) to automatically identify anatomical markers that may be used to identify historical data (e.g., historical data 194 and / or medical data 196) that pertains to the medical issue of concern for the patient 102. The identification of anatomical markers allows for subsequent implementation of other processes (e.g., processes 125A-n) to determine / infer one or more medical conditions regarding the patient.

[0072] At step 930, rather than presenting (e.g., on the complete data screen 160A-n) the second medical information for a doctor (e.g., doctor 103) to spend time examining them to formulate a diagnosis, the first medical information including relevant / important information (e.g., images 172A-n, text 176A-n, results 179A-n) can be identified and presented on a summary screen (e.g., summary screen 150A). AI / ML technologies can further be applied to the first medical data and / or the second medical data to automatically generate (e.g., by the status component 130) a potential result / diagnosis (e.g., a result 179A-n / potential diagnosis 178A).The potential diagnosis can be presented either on the summary screen or on the full data screen, indicating (a) a degree of confidence in the automatically generated potential diagnosis and / or (b) whether the patient has or does not have the medical condition..

[0073] At step 940, an input can be received on the summary screen from the doctor regarding whether the doctor agrees or not with the diagnosis. When examining the information presented on the summary screen, the doctor can access the secondary medical information in case the doctor would need information other than that provided by the primary medical information. The additional information (e.g., additional information 155A-n) can be presented on the summary screen in the form of contextual windows, incorporated into existing text, additional existing images replace existing images and text, and so on. Depending on the degree of interaction / editing by the physician with the first medical information and / or the second medical information, the modifications, replacements, annotations, etc. may result in the presentation of third medical information on the summary screen and / or on the full data screen, whereby the third medical information reflects the updates, etc., that have been made by the physician. The diagnosis may be continuously updated based on the physician's modification of the presented medical information.

[0074] At step 950, a confirmation (e.g., confirmation 142A-n) may be received from the physician, whereby a report (e.g., report 165A-n) may be generated (e.g., by report component 135) for review (e.g., on summary screen 150A-n). The physician may further modify the report as needed and, upon completion, may print / export the report.

[0075] As used herein, the terms "infer," "inference," "determine," and the like, generally refer to the process of reasoning or inferring system, environment, and / or user states from a set of observations, as captured via events and / or data. Inference may be used to identify a specific context or action, or may generate a probability distribution over states, for example. Inference may be probabilistic, i.e., computing a probability distribution over states of interest based on consideration of data and events. Inference may also refer to techniques used to compose higher-level events from a set of events and / or data.Such inference results in the construction of new events or actions from a set of observed events and / or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and data come from one or more event and data sources.

[0076] According to the various embodiments presented herein, various components included in the MDPS 110, the presentation component 115, the state component 130, the summary component 120, and the like, may include reasoning and AI / ML technologies and techniques (e.g., processes 125A-n) that use probabilistic and / or statistical analysis to predict or infer an action that a user wishes to be performed automatically. The various embodiments presented herein may use various machine learning-based schemes to implement various aspects thereof. For example, a process 125A-n (e.g., by a pre presentation component 115, a summary component 120, a status component 130 and the like) for determining the content of the historical data 194 / medical data 196 regarding the content of the input data 108A-n (e.g., to determine a result 179A-n), a process 125A-n (e.g., by a presentation component 115, a summary component 120, a status component 130, and the like) for determining information to be presented on a summary screen 150A-n, a process 125A-n (e.g., by the presentation component 115, the summary component 120, the status component 130 and the like) for automatically generating a potential diagnosis 178A-n based on the determined correlation(s) between the input data 108A-n and the historical data 194A-n / 196A-n medical data, and other analogs, as mentioned earlier in this document, can be facilitated via an automatic classifier system and process.

[0077] A classifier is a function that maps an input attribute vector, x = (xl, x2, x3, x4, xn), to a class label class(x). The classifier may also generate a confidence that the input belongs to a class, i.e., f(x) = confidence(class(x)). Such classification may use probabilistic and / or statistical analysis (e.g., taking into account analysis utilities and costs) to predict or infer an action that a user wishes to be performed automatically (e.g., by identifying the respective characteristics presented in the input data 108A-n and creating information for the summary 150A-n and diagnostic 178A-n screens, and associated operations).

[0078] A support vector machine (SVM) is an example of a classifier that can be used. The SVM works by finding a hypersurface in the space of possible inputs that optimally separates triggering input events from non-triggering events. Intuitively, this makes classification correct for testing data that is close, but not identical to the training data. Other supervised and unsupervised pattern classification approaches include, for example, naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models allowing for the use of different independence models. Classification as used in this paper includes statistical regression, which is used to develop priority models.

[0079] As readily apparent from the subject description, the various embodiments may utilize classifiers that are explicitly trained (e.g., via generic training data) as well as implicitly trained (e.g., via observation of user behavior, receipt of extrinsic information). For example, the SVMs are configured via a training phase or training within a feature selection module and a classifier builder. Thus, the classifier(s) may be used to automatically learn and perform a number of functions, including, but not limited to, determining, according to predetermined criteria, the content of the input data 108A-n and historical data 194A-n / medical data 196A-n, and generating results / diagnoses 178A-n based thereon, for example.

[0080] As described above, inferences may be made and automated operations may be performed, based on numerous pieces of information. For example, whether sufficient context is available to infer, with a high degree of confidence, a correlation between the content of the input data 108A-n and the historical data 194A-n / medical data 196A-n, whether a result / diagnosis 178A-n has been correctly applied to the input data 108A-n, and the like, to enable a diagnosis to be made from the images 172A-n and text 176A-n presented on the summary screen 150A-n.

[0081] In review, the different embodiments presented in this document allow:

[0082] a) selecting the respective processes 125A-n to be used depending on the acquisition parameters (e.g., in the input data 108A-n, applied by the physician 103, and the like),

[0083] [b) the implementation of the respective processes 125A-n facilitates the pre-treatment of the input data 108A-n for automatically identifying anatomical markers that can be used to identify historical data 194 and / or medical data 196 that relate to the medical issue of concern for the patient 102, and

[0084] c) implementing the respective processes 125A-n throughout the MDPS 110 to standardize the presentation of rendering medical information on the summary screens 150A-n and the full data screens 160A-n, as well as the generation of reports 165A-n. For example, one or more processes 125A-n may be used to communicate between different presentation systems about how a result should be presented.

[0085] The various embodiments presented herein enable the generation and operation of the MDPS 110 that can be incorporated into a complete medical ecosystem, for example, from an initial worklist based on the medical condition / symptoms / input data 108A-n of the patient 102, as well as providing the capability to refine the automatically generated results by implementing the respective processes 125A-n, and further integrate the MDPS 110 into external reporting tools (e.g., PACS, RISS) and medical services.

[0086] Further, the various embodiments presented herein allow for a simplified interface (e.g., summary screens 150A-n) for the physician 103 to review the automated results generated by the implementation of the processes 125A-n, in a compact visual representation, while still providing a more detailed presentation either through the use of pop-up windows and / or by switching between the summarized data presented on the summary screen 150A-n and the full data presented on the full data screen 160A-n. Additionally, when implemented in a medical condition review ecosystem, the MDPS 110 allows for easy modification of medical data / results / diagnoses, as well as implementation in searching, reviewing, generating reports for local and external use, patient reports, etc. Additionally, more than one 103A-n physician may use the MDPS 110. EXAMPLES OF APPLICATIONS AND USE

[0087] Referring next to Figures 10 and 11, a detailed description is provided with additional context for the one or more embodiments described herein with Figures 1-9.

[0088] In order to provide additional context for various embodiments described herein, [Fig. 10] and the following discussion are intended to provide a brief general description of a suitable computing environment 1000 in which the various embodiments described herein may be implemented. Although the embodiments have been described above in the general context of computer-executable instructions capable of execution on one or more computers, those skilled in the art will recognize that the embodiments may also be implemented in combination with other program modules and / or as a combination of hardware and software.

[0089] Generally, program modules comprise routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods may be practiced with other computer system configurations, including single- or multi-processor computer systems, minicomputers, mainframe computers, IoT devices, distributed computing systems, as well as personal computers, laptop computers, portable computing devices, programmable or microprocessor-based consumer electronics devices, and the like, each of which may be operatively coupled to one or more associated devices.

[0090] The embodiments illustrated herein may also be practiced in distributed computing environments in which certain tasks are performed by remote processing devices that are connected via a communications network. In a distributed computing environment, program modules may be located in local and remote memory storage devices.

[0091] Computing devices generally include a variety of media, which may include computer-readable storage media, machine-readable storage media, and / or communication media, both of which terms are used herein differently from each other as follows. Computer-readable storage media or machine-readable storage media may be any available storage media that can be accessed by the computer and includes both volatile and non-volatile media, removable and non-removable media.By way of example, and without limitation, computer-readable storage media or machine-readable storage media may be implemented in connection with any method or technology for storing information such as computer-readable or machine-readable instructions, program modules, structured data, or unstructured data.

[0092] Computer-readable storage media can include, but are not limited to, random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray disc (BD) or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, semiconductor disks or other semiconductor disk storage devices, or other tangible and / or non-transitory media that can be used to store the desired information.In this regard, the terms "tangible" or "non-transitory" as applied herein to storage, memory or computer-readable media, are to be understood as excluding only the propagation of transient signals per se as modifiers and do not waive all rights to storage, memory or computer-readable storage media that do not only propagate transient signals per se.

[0093] The computer-readable storage media may be accessed by one or more local or remote computing devices, e.g., via access requests, queries, or other data retrieval protocols, for various operations regarding the information stored by the media.

[0094] Communication media generally incorporate computer-readable instructions, data structures, program modules, or other structured or unstructured data into a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any transmission of information or other transport medium. The term "modulated data signal" or signals refers to a signal having one or more of its characteristics defined or modified so as to encode information in one or more signals. By way of example, and not as a limitation, communication media includes wired media, such as a cable network or a direct wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media.

[0095] Referring again to [Fig. 10], the exemplary environment 1000 for implementing various embodiments of the aspects described herein includes a computer 1002, the computer 1002 including a processing unit 1004, a system memory 1006, and a system bus 1008. The system bus 1008 couples system components including, but not limited to, a system memory 1006 to the processing unit 1004. The processing unit 1004 may be any of a variety of commercially available processors and may include cache memory. Dual microprocessors and other multiprocessor architectures may also be used as the processing unit 1004.

[0096] The system bus 1008 may be any of several types of bus structures which may further interconnect to a memory bus (with or without a memory controller), a peripheral bus and a local bus using any of a variety of commercially available bus architectures. The system memory 1006 includes ROM 1010 and RAM 1012. A basic input / output system (BIOS) may be stored in non-volatile memory such as ROM, erasable programmable read-only memory (EPROM), EEPROM, the BIOS of which contains the basic routines that help transfer information between elements within the computer 1002, such as during startup. The RAM 1012 may also include high-speed RAM such as static RAM for caching data.

[0097] The computer 1002 further includes an internal hard disk drive (HDD) 1014 (e.g., EIDE, SATA), one or more external storage devices 1016 (e.g., a magnetic floppy disk drive (FDD) 1016, a USB flash drive or USB drive, a memory card reader, etc.), and an optical disk drive 1020 (e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.). While the internal HDD 1014 is illustrated as being located inside the computer 1002, the internal HDD 1014 may also be configured for external use in a suitable setting (not shown). Additionally, although not shown in environment 1000, a solid-state drive (SSD) could be used in addition to or instead of an HDD 1014. The HDD 1014, the external storage device(s) 1016, and the optical disk drive 1022 may be connected to the system bus 1008 by an HDD interface 1024, an external storage interface 1026, and an optical drive interface 1028, respectively. The interface 1024 for external drive implementations may comprise at least one or both of Universal Serial Bus (USB) and Institute of Electrical and Electronics Engineers (IEEE) 1094 interface technologies. Other external drive connection technologies are contemplated in the embodiments described herein.

[0098] The drives and their associated computer-readable storage media provide non-volatile storage of data, data structures, computer-executable instructions, etc. For computer 1002, the drives and storage media enable storage of all data in a suitable digital format. Although the above description of the computer-readable storage media refers to the respective types of storage devices, those skilled in the art should understand that other types of computer-readable storage media, whether currently existing or developed in the future, could also be used in the exemplary operating environment, and further, any such storage media may contain computer-executable instructions for performing the methods described herein.

[0099] A number of program modules may be stored in the drives and RAM 1012, including an operating system 1030, one or more application programs 1032, other program modules 1034, and program data 1036. All or portions of the operating system, applications, modules, and / or data may also be cached in RAM 1012. The systems and methods described herein may be implemented using various commercially available operating systems or combinations of operating systems.

[0100] The computer 1002 may optionally include emulation technologies. For example, a hypervisor (not shown) or other intermediary may emulate a hardware environment for the operating system 1030, and the emulated hardware may optionally be different from the hardware illustrated in [Fig. 10]. In such an embodiment, the operating system 1030 may include one of several virtual machines (VMs) hosted on the computer 1002. In addition, the operating system 1030 may provide runtime environments, such as the Java Runtime Environment or the .NET framework, for the applications 1032. The runtime environments are consistent execution environments that allow the applications 1032 to run on any operating system that includes the runtime environment. Similarly, the operating system 1030 may support containers, and the applications 1032 can come in the form of containers, which are lightweight, self-contained, executable software packages that include, for example, code, a runtime environment, system tools, system libraries, and settings for an application.

[0101] Further, the computer 1002 may include a security module, such as a Trusted Processing Module (TPM). For example, with a TPM, boot components hash subsequent boot components and wait for a match of the results with the secure values ​​before loading a subsequent boot component. This process may occur at any layer of the code execution stack of the computer 1002, e.g., applied at the application execution level or at the operating system (OS) kernel level, thereby enabling security at any level of code execution.

[0102] A user may input commands and information into the computer 1002 via one or more wired / wireless input devices, e.g., a keyboard 1038, a touchscreen 1040, and a pointing device, such as a mouse 1042. Other input devices (not shown) may include a microphone, an infrared (IR) remote control, a radio frequency (RF) remote control, or other remote control, a joystick, a virtual reality controller and / or a virtual reality headset, a gamepad, a stylus, an image input device, e.g., one or more cameras, a gesture sensor input device, a vision motion sensor input device, an emotion or face detection device, a biometric input device, e.g., a fingerprint or iris scanner, or the like.These input devices and others are often connected to the processing unit 1004 via an input device interface 1044 which may be coupled to the system bus 1008, but may be connected by other interfaces, such as a parallel port, an IEEE 1094 serial port, a game port, a USB port, an IR interface, a BLUETOOTH® interface, etc.

[0103] A monitor 1046 or another type of display device may also be connected to the system bus 1008 via an interface, such as a video adapter 1048. In addition to the monitor 1046, a computer generally includes other output peripherals (not shown), such as speakers, printers, etc.

[0104] The computer 1002 may operate in a networked environment using logical connections via wired and / or wireless communications with one or more remote computers, such as one or more remote computers 1050. The one or more remote computers 1050 may be a workstation, a server computer, a router, a personal computer, a laptop computer, a microprocessor-based entertainment device, a peer device, or another common network node, and generally includes several or all of the elements described in relation to the computer 1002. tively to the computer 1002, although for brevity only one memory / storage device 1052 is illustrated. The logical connections shown include wired / wireless connectivity to a local area network (LAN) 1054 and / or larger networks, e.g., a wide area network (WAN) 1056. Such LAN and WAN network environments are common in offices and businesses and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, e.g., the Internet.

[0105] When used in a LAN environment, the computer 1002 may be connected to the local area network 1054 via a wired and / or wireless communication network interface or adapter 1058. The adapter 1058 may facilitate wired or wireless communication with the LAN 1054, which may also have a wireless access point (AP) disposed thereon to communicate with the adapter 1058 in a wireless mode.

[0106] When used in a WAN environment, the computer 1002 may include a modem 1060 or may be connected to a WAN communications server 1056 via other means to establish communications over the WAN 1056, for example, via the Internet. The modem 1060, which may be an internal or external wired or wireless device, may be connected to the system bus 1008 via the input device interface 1044. In a networked environment, the program modules shown with respect to the computer 1002 or portions thereof may be stored in the remote memory / storage device 1052. It should be noted that the network connections shown are examples and other means of establishing a communications link between the computers may be used.

[0107] When used in a LAN or WAN environment, the computer 1002 may access cloud storage systems or other network-attached storage systems in addition to or instead of the external storage devices 1016 as described above. Generally, a connection between the computer 1002 and a cloud storage system may be established over a LAN 1054 or a WAN 1056, for example, by the adapter 1058 or the modem 1060, respectively. When connecting the computer 1002 to an associated cloud storage system, the external storage interface 1026 may, using the adapter 1058 and / or the modem 1060, manage the storage provided by the cloud storage system as it would other types of external storage. For example, the external storage interface 1026 may be configured to provide access to cloud storage sources as if those sources were physically connected to the computer 1002.

[0108] The computer 1002 may be operative to communicate with any wireless device or entity operatively disposed in a wireless communication communication, for example a printer, a scanner, a desktop and / or portable computer, a portable data assistant, a communication satellite, any equipment or location associated with a wireless detectable tag (e.g., a kiosk, a newspaper kiosk, a store shelf, etc.), and a telephone. This may include Wireless Fidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, the communication may be a predefined structure such as with a conventional network or simply an ad hoc communication between at least two devices.

[0109] The above description includes non-limiting examples of the various embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for the purpose of describing the disclosed subject matter, and those skilled in the art may recognize that other combinations and permutations of the various embodiments are possible. The disclosed subject matter is intended to encompass all such alterations, modifications, and variations as fall within the spirit and scope of the appended claims.

[0110] Referring now to the details of one or more elements illustrated in [Fig.l 1], an illustrative cloud computing environment 1100 is shown. [Fig. 11] is a block diagram of a computing environment 1100 with which the disclosed object may interact. The system 1100 includes one or more remote components 1110. The remote component(s) 1110 may be hardware and / or software (e.g., threads, processes, computing devices). In some embodiments, the remote component(s) 1110 may be a distributed computing system, connected to a local auto-scaling component and / or programs that utilize the resources of a distributed computing system, via a communication framework 1140.The communication framework 1140 may include wired network devices, wireless network devices, mobile devices, portable devices, radio access network devices, gateway devices, femtocell devices, servers, etc.

[0111] The system 1100 also includes one or more local / local components 1120. The local / local component(s) 1120 may be hardware and / or software (e.g., threads, processes, computing devices). In some embodiments, the local / local component(s) 1120 may include an auto-scaling component and / or programs that communicate / use the remote resources 1110 and 1120, etc., connected to a remotely located distributed computing system via a communication framework 1140.

[0112] Possible communication between one or more remote components 1110 and one or more local components 1120 may take the form of a data packet adapted to be transmitted between two or more computer processes. Another possible communication between one or more remote component(s) 1110 and one or multiple local / local component(s) 1120 may be in the form of circuit-switched data adapted to be transmitted between two or more computer processes in radio time slots. The system 1100 includes a communication framework 1140 that may be used to facilitate communications between the remote component(s) 1110 and the local / local component(s) 1120, and may include an air interface, for example the Uu interface of a UMTS network, via a Long Term Evolution (LTE) network, etc. The remote component(s) 1110 may be operatively connected to one or more remote data stores 1150, such as a hard drive, an SSD, a SIM card, a device memory, etc., which may be used to store information on the remote component(s) 1110 side of the communication framework 1140.Similarly, the local component(s) 1120 may be operatively connected to one or more local data stores 1130, which may be used to store information on the local component(s) 1120 side of the communication framework 1140.

[0113] With respect to the various functions performed by the components, devices, circuits, systems, etc. described above, the terms (including a reference to a "means") used to describe such components are intended to also include, unless otherwise indicated, any structure that performs the specified function of the disclosed component (e.g., a functional equivalent), even if it is not structurally equivalent to the disclosed structure. Moreover, even though a particular feature of the disclosed subject matter may have been disclosed with respect to only one of several implementations, that feature may be combined with one or more other features of the other implementations, as may be desired and advantageous for any particular or given application.

[0114] The terms "exemplary" and / or "demonstrative" as used herein mean to serve as an example, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. Furthermore, any aspect or design described herein as "Exemplary" and / or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it intended to exclude equivalent structures and techniques known to those skilled in the art. Furthermore, to the extent that the terms "comprises," "has," "contains," and other similar words are used either in the detailed description or in the claims, these terms are intended to be inclusive—in a manner similar to the term "comprising" as an open transition word—without excluding any additional or other elements.

[0115] The term "or," as used herein, is intended to mean an inclusive "or" rather than an exclusive "or." For example, the phrase "A or B" is intended to include instances of A, B and both A and B. Further, the articles "a" and "an" as used in this application and the appended claims should generally be interpreted to mean "one or more" unless otherwise indicated or clear from the context to be directed to a singular form.

[0116] The term "set" as used herein excludes an empty set, i.e., a set containing no elements. Thus, a "set" in the subject disclosure comprises one or more elements or entities. Similarly, the term "group," as used herein, refers to a set of one or more entities.

[0117] The terms "first," "second," "third," and so on, as used in the claims, unless otherwise indicated by the context, are for clarity only and do not otherwise indicate or imply any order in time. For example, "a first determination," "a second determination," and "a third determination" do not indicate or imply that the first determination must be made before the second determination, or vice versa, etc.

[0118] As used in this disclosure, in some embodiments, the terms "component," "system," and the like are intended to refer to, or include, a computer-related entity or an entity related to an operational apparatus with more specific functionality(s), wherein the entity may be either hardware, a combination of hardware and software, software, or executing software. By way of example, a component may be, but is not limited to, a process executing on a processor, a processor, an object, an executable, a thread of execution, computer-executable instructions, a program, and / or a computer. By way of illustration and not limitation, an application running on a server as well as the server may be a component.

[0119] One or more components may reside in a process and / or a thread of execution and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may execute from various computer-readable media on which various data structures are stored. The components may communicate via local and / or remote processes, for example in accordance with a signal comprising one or more data packets (e.g., data from a component interacting with another component in a local system, a distributed system and / or across a network such as the Internet with other systems via the signal).As another example, a component may be a device with specific functionality provided by mechanical parts actuated by electrical or electronic circuits, which is operated by a software application or firmware application executed by a processor, wherein the processor may be internal or external to the device, and executes at least a portion of the software application or firmware. Such as . As yet another example, a component may be a device that provides specific functionality via electronic components without mechanical parts, the electronic components may include a processor for executing software or firmware that provides at least in part the functionality of the electronic components. Although various components have been illustrated as separate components, it will be understood that multiple components may be implemented as a single component, or that a single component may be implemented as multiple components, without departing from the exemplary embodiments.

[0120] The term "facilitate" as used herein is in the context of a system, device, or component "facilitating" one or more actions or operations, with respect to the nature of complex computing environments in which multiple components and / or multiple devices may be involved in certain computing operations. Non-limiting examples of actions that may or may not involve multiple components and / or multiple devices include transmitting or receiving data, establishing a connection between devices, determining intermediate results toward achieving a result, etc. In this regard, a computing device or component may facilitate an operation by playing some role in performing the operation.When the operations of a component are described herein, it is therefore to be understood that when the operations are described as being facilitated by the component, the operations may possibly be completed with the cooperation of one or more other computing devices or components, such as, but not limited to, sensors, antennas, audio and / or visual output devices, other devices, etc.

[0121] Further, the various embodiments may be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques to produce software, firmware, hardware, or any combination thereof for controlling a computer to implement the disclosed subject matter. The term "article of manufacture," as used herein, is intended to encompass a computer program accessible from any computer-readable (or machine-readable) device or computer-readable (or machine-readable) storage / communication medium. For example, computer-readable storage media may include, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic tapes), optical disks (e.g., compact disc (CD), digital versatile disc (DVD))., smart cards and flash memory devices (e.g., card, USB key, key reader). Of course, those skilled in the art will recognize that many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments.

[0122] Furthermore, terms such as "mobile device equipment", "mobile station", "mobile", "subscriber station", "access terminal", "terminal", "handset", "communication device", "mobile device" (and / or terms representing similar terminology) may refer to a wireless apparatus used by a subscriber or mobile device of a wireless communication service to receive or transmit data, control, voice, video, audio, games or virtually any data stream or signaling stream. The above terms are used interchangeably herein and with reference to the associated drawings.Similarly, the terms "access point (AP)", "base station (BS)", "BS transceiver", "BS device", "cell site", "cell site device", "gNode B (gNB)", "evolved Node B (eNode B, eNB)", "home Node B (HNB)" and the like, refer to wireless network components or devices that transmit and / or receive data, commands, voice, video, audio, games or virtually any other data stream or signaling stream from one or more subscriber stations. The data and signaling streams may be packet-based or frame-based.

[0123] Furthermore, the terms "device," "communication device," "mobile device," "subscriber," "client entity," "consumer," "customer entity," "entity," and the like are used interchangeably throughout the text unless the context warrants particular distinctions between the terms. It should be noted that these terms may refer to human entities or to automated components supported by artificial intelligence (e.g., an ability to make inferences based on complex mathematical formalisms), which may provide simulated vision, sound recognition, etc.

[0124] It should be noted that although various aspects and embodiments are described herein in the context of 5G or other next-generation networks, the disclosed aspects are not limited to a 5G implementation, and may be applied in other next-generation network implementations, such as sixth generation (6G) or other wireless systems. In this regard, aspects or features of the disclosed embodiments may be exploited in virtually any wireless communication technology.These wireless communication technologies may include Universal Mobile Telecommunications System (UMTS), Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), Wideband CDMA (WCMDA), CDMA2000, Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Multi-Carrier CDMA (MC-CDMA), Single-Carrier CDMA (SC-CDMA), Single-Carrier FDMA (SC-FDMA), Orthogonal Frequency Division Multiplexing (OFDM), Fourier Transform Spread OFDM. discrete (DFT-spread OFDM), filter bank-based multicarrier (FBMC), zero-tailed DFT-spread OFDM (ZT DFT-s-OFDM), generalized frequency division multiplexing (GFDM), fixed mobile convergence (FMC), universal fixed mobile convergence (UFMC), single-word OFDM (UW-OFDM), single-word DFT-spread OFDM (UW DFT-Spread-OFDM), cyclic prefix OFDM (CP-OFDM), resource block filtered OFDM, Wireless Fidelity (Wi-Fi), global interoperability for microwave access (WiMAX), wireless local area network (WLAN), general packet radio service (GPRS), enhanced GPRS, 3GPP, long term evolution (LTE), 5G, 3GPP2, ultra-mobile broadband (UMB), high-speed packet access (HSPA), high-speed packet access Advanced (HSPA+), High-Speed ​​Downlink Packet Access (HSDPA), High-Speed ​​Uplink Packet Access (HSUPA),Zigbee or other 802.12 technology from the Institute of Electrical and Electronics Engineers (IEEE).

[0125] It is to be understood that when an element is referred to as being "coupled" to another element, it may describe one or more different types of coupling including, but not limited to, chemical coupling, communicative coupling, electrical coupling, electromagnetic coupling, operational coupling, optical coupling, physical coupling, thermal coupling, and / or another type of coupling. Similarly, it is to be understood that when an element is referred to as being "connected" to another element, it may describe one or more different types of connection, including, but not limited to, electrical connection, electromagnetic connection, operational connection, optical connection, physical connection, thermal connection, and / or another type of connection.

[0126] The description of the illustrated embodiments of the subject invention as provided herein, including what is described in the summary, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. Although specific embodiments and examples are described herein for illustrative purposes, various modifications are possible and are considered within the scope of these embodiments and examples, as may be recognized by those skilled in the art. In this regard, although the subject matter has been described herein in connection with various embodiments and corresponding drawings, where appropriate, it is to be understood that other similar embodiments may be used or modifications and additions may be made to the disclosed embodiments to achieve the same, similar, alternative, or replacement function of the disclosed subject matter without departing therefrom.Therefore, the disclosed subject matter should not be limited to only one embodiment described herein, but rather should be interpreted in its breadth and scope in accordance with the appended claims below..

[0127]

Claims

Claims

1. A system comprising: a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate performing operations, including: receiving first information regarding a medical condition of a patient; examining the first information to identify the medical condition; generating a summary of the first information; and presenting the summary to facilitate determining the medical condition.

2. The system of claim 1, wherein the first information comprises at least one medical image or information about a medical condition.

3. The system of claim 1, wherein the operations further comprise: automatically determining a diagnosis of the patient's medical condition.

4. The system of claim 3, wherein the diagnosis of the medical condition is accompanied by a degree of confidence in the diagnosis.

5. The system of claim 1, wherein the operations further comprise: detecting a modification applied to the summary of the first information, wherein the modification comprises adding or deleting information from the summary of the first information.

6. The system of claim 5, wherein the operations further comprise: updating the first information to generate second information, wherein the second information comprises the modification applied to the summary of the first information.

7. The system of claim 6, wherein the operations further comprise: presenting a diagnosis of the medical condition; receiving confirmation of the diagnosis; and in response to receiving confirmation of the diagnosis, generating a report presenting a summary of the second information.

8. The system of claim 1, wherein the operations further comprise: presenting a diagnosis of the medical condition; receiving confirmation of the diagnosis; and in response to receiving confirmation of the diagnosis, generating a report presenting a summary of the first information.

9. A system, comprising: a processor; and a memory that stores executable instructions that, when executed by the processor, facilitate the performance of operations, including: automatically compiling and concatenating multiple types and sources of information received from multiple sources; automatically generating a structured summary of the information; and automatically displaying the structured summary, wherein displaying the structured summary further comprises: presenting information in an integrated manner facilitating direct review of the structured summary; receiving a change to the information; and updating information associated with the change in accordance with the change.

10. The system of claim 9, wherein the first information comprises a medical image and / or information about a medical condition.

11. The system of claim 9, wherein the operations further comprise: automatically generating analyses and measurements of the medical imaging examination combined with other patient information.

12. The system of claim 11, wherein the analysis and measurements are accompanied by a degree of confidence in the AI that generated them.

13. The system of claim 9, wherein the operations further comprise: detecting a modification applied to the summary of the first information / initial information, wherein the modification comprises adding, modifying or deleting information from the summary of the first information.

14. The system of claim 13, wherein the operations com- further take: updating the first information / initial information to generate second information / updated information, wherein the second information / updated information includes the change applied to the summary of the first information / initial information.

15. The system of claim 14, wherein the operations further comprise: presenting all the information (initial and / or modified / updated) as a summary of the analysis and measurements being the source of inputs to build a report with an external solution generating and exporting all the information of this summary from the system itself.

16. The system of claim 9, wherein the information comprises measurements and / or images generated using one or more artificial intelligence (AI) technologies.

17. The system of claim 9, wherein the multiple sources comprise a medical application and / or a medical system.

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